A computer hardware system associated with a hybrid cloud including a private cloud and a public cloud has a hardware processor configured to perform the following executable operations. A plurality of data sets of unprocessed data is randomly sampled to generate a plurality of random samples respectively associated with each of the plurality of data sets. The plurality of random samples is pre-processed, using a machine learning engine, to identify hidden correlations contained therein. The hidden correlations are evaluated against a confidentiality model to characterize each of the plurality of segments as to confidentiality. The data sets are segmented by assigning the data sets to one of the private cloud or the public cloud based upon the evaluating. Data sets assigned to the private cloud based upon the segmenting are processed using the private cloud.

An Artificial Intelligence (AI) ergonomic assessment and positioning system that analyzes remote workspace data, identifies objects that are improperly positioned, oriented, and/or have undesirable settings, and automatically adjusts, moves, sets, and/or provides automatic guidance for the adjustment, movement, and/or setting of target objects in the remote workspace.

The present disclosure provides a system for automating graphical testing during video game development. The system can use Deep Convolutional Neural Networks (DCNNs) to create a model to detect graphical glitches in video games. The system can use an image, a video game frame, as input to be classified into one of defined number of classifications. The classifications can include a normal image and one of a plurality of different kinds of glitches. In some embodiments, the glitches can include corrupted textures, including low resolution textures and stretched textures, missing textures, and placeholder textures. The system can apply a confidence measure to the analysis to help reduce the number of false positives.

Source code of any application may be edited/modified to accommodate new changes. The changes in the source code may also affect static analysis alarms that were generated for the original source code. Changes in the source code may result in newly generated alarms, some of the alarms in the original source code may repeat in the new source code. Many of the repeated alarms get suppressed using appropriate techniques. The repeated alarms that remain after the suppression, and the newly generated alarms together form the delta alarms. Each of the delta alarms may have been generated due to different reasons. Classification of the delta alarms is performed based on reasons/causes for their generation. The system further performs ranking of the classes of the delta alarms and thus ranking of the delta alarms. Further, the system groups the alarms having common cause and reports the delta alarms with their causes.

In one aspect, a computerized method of computer vision based dynamic universal fashion ontology fashion rating and recommendations includes the step of receiving one or more user-uploaded digital images. The method includes the step of implementing an image classifier on the one or more user-uploaded digital images, to classify a set of user-uploaded fashion content of the one or more user-upload digital images. The method includes the step of receiving a set of fashion rules input by a domain expert. The set of rules determine a set of apparel to match with the set of user-uploaded fashion content, generating a dynamic universal fashion ontology with the image classier and a text classier. The dynamic universal fashion ontology comprises an ontology of set of mutually exclusive attribute classes. The method includes the step of using the dynamic universal fashion ontology to train a specified machine learning based fashion classifications. The method includes the step of using an active learning pipeline to keep the universal fashion ontology up-to-date. The method includes the step of using graphical representation and game theory-based algorithm for outfit generation. The method includes the step of providing an automatic outfit generator, wherein the automatic outfit generator: based on the set of user-uploaded fashion content that is output by the image classifier, matches the set of user-uploaded fashion with a ranked set of apparel suggestions that are based on the set of fashion rules and the specified machine learning based fashion classifications, wherein the automatic outfit generator implements a greedy algorithm to determine the most optimal path in the specified machine learning based fashion classifications to generate each suggested piece of apparel in the ranked set of apparel suggestions. The method includes the step of, based on the highest ranked suggested piece of apparel in the ranked set of apparel suggestions, generating an outfit suggestion. The method includes the step of ranking based on lifestyle parameters including but not limited to weather, brand affinity, brand popularity and novelty of style.

An approach is provided for progressive training of long-lived, evolving machine learning architectures. The approach involves, for example, determining alternative paths for the evolution of the machine learning model from a first architecture to a second architecture. The approach also involves determining one or more migration step alternatives in the alternative paths. The migration steps, for instance, include architecture options for the evolution of the machine learning model. The approach further involves processing data using the options to determine respective model performance data. The approach further involves selecting a migration step from the one or more migration step alternatives based on the respective model performance data to control a rate of migration steps over a rate of training in the evolution of the machine learning model. The approach further involves initiating a deployment the selected migration step to the machine learning model.

A highly versatile data processing is implemented on data collected in a manufacturing process. A data processing device includes: a calculation part configured to collect a plurality of data groups associated with a predetermined step of a process, and calculate effects in the predetermined step for each of the plurality of data groups; a dividing part configured to divide a feature space such that a distribution of each of the plurality of data groups associated with the predetermined step in the feature space is classified for each of the calculated effects; and an output part configured to output specific data that specifies respective regions of the divided feature space.

A method for testing a technical system. The method includes: tests are carried out with the aid of a simulation of the system, the tests are evaluated with respect to a fulfillment measure of a quantitative requirement on the system and an error measure of the simulation, on the basis of the fulfillment measure and error measure, a classification of the tests as either reliable or unreliable is carried out.

A method for testing a technical system. The method includes: tests are carried out with the aid of a simulation of the system, the tests are evaluated with respect to a fulfillment measure of a quantitative requirement on the system and an error measure of the simulation, on the basis of the fulfillment measure and error measure, a classification of the tests as either reliable or unreliable is carried out, and a test database is improved on the basis of the classification.

Provided are an image recognition apparatus, an image recognition method, and a program for enabling recognition of many kinds of objects with high precision. An overall recognition unit executes, for at least one given object, a process of recognizing the position of the object in an image. A partial image extraction unit extracts, from the image, a partial image which is a part of the image associated with the recognized position. A partial recognition unit executes a process of recognizing what is one or more objects represented by the partial image, the one or more objects including an object other than the given object the position of which is recognized.